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Archives for April 2019

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End-To-End Topic Modeling in Python towardsdatascience.com

Published April 26, 2019 under Data Science

Topic Model: In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.

Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.

Machine Learning, NLP, Topic Modelling

When to ‘Buy the Dip’ towardsdatascience.com

Published April 26, 2019 under Trading

“Buy the dip” — it’s a frustratingly simple piece of advice. Like most pieces of advice, it’s easier said than done and the giver of such advice has probably not attempted to practice what they preach. It induces FOMO, which leads to the “hope trade”, when the “hope trade” goes awry you’re stuck as the “long term investor” who “really believes in the company’s mission”.

Markov Models, Python, Volatility

Building a Robinhood Stock Trading Bot medium.com

Published April 26, 2019 under Trading

The bot is written in Python and relies on two core libraries for the majority of its functionality: robin-stocks and ta. robin-stocks is a library that interacts with the Robinhood API and allows one to execute buy and sell orders, get real time ticker information, and more. ta is a technical analysis library that also incorporates the Python Pandas library to generate indicators from stock data.

Algorithmic Trading, Pandas, Technical Analysis

OpenCV-Python Cheat Sheet: From Importing Images to Face Detection fritz.ai

Published April 21, 2019 under Machine Learning

Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.

Computer Vision, OpenCV, Python

Pandas DataFrames for Data Analysis kite.com

Published April 19, 2019 under Data Science

In this post, we’ll learn about Pandas, a high-performance open-source package for doing data analysis in Python.

We’ll cover:

  • What Pandas is and why should you use it.
  • What a Pandas DataFrame is.
  • Creating and viewing a DataFrame.
  • Manipulating data in a DataFrame.

DataFrame, Pandas

How to Automate Tasks on GitHub With Machine Learning for Fun and Profit towardsdatascience.com

Published April 19, 2019 under Machine Learning

A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

Python, TensorFlow

Awesome Data Science with Python github.com

Published April 19, 2019 under Python

A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. So. Much. Python.

Awesome, Data Science

PyTorch Sentiment Analysis github.com

Published April 19, 2019 under Machine Learning

Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.

The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.

Data Science, Python, PyTorch

Text Classification in Python Using spaCy dataquest.io

Published April 19, 2019 under Machine Learning

NLP, spaCy, Text Classification

Vaex: A DataFrame with super strings towardsdatascience.com

Published April 19, 2019 under Data Science

String manipulations are an essential part of Data Science. The latest release of Vaex adds incredibly fast and memory efficient support for all common string manipulations. Compared to Pandas, the most popular DataFrame library in the Python ecosystem, string operations are up to ~30–100x faster on your quadcore laptop, and up to a 1000 times faster on a 32 core machine.

Pandas, Vaex

Introduction to Anomaly Detection in Python floydhub.com

Published April 19, 2019 under Python

The very basic idea of anomalies is really centered around two values – extremely high values and extremely low values. Then why are they given importance? In this article, we will try to investigate questions like this. We will see how they are created/generated, why they are important to consider while developing machine learning models, how they can be detected.

Anomaly Detection, Time Series Analysis

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Published April 18, 2019 under Books

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.

Matplotlib, Numpy, SciPy

Learning Python, 5th Edition

Published April 18, 2019 under Books

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.

Python

Python for NLP: Introduction to the TextBlob Library

Published April 18, 2019 under Data Science

In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.

NLP, TextBlob

Pyodide: Bringing the scientific Python stack to the browser mozilla.org

Published April 18, 2019 under Python

Pyodide is an experimental project from Mozilla to create a full Python data science stack that runs entirely in the browser.

Programming

Kalman and Bayesian Filters in Python github.com

Published April 17, 2019 under Data Science

Bayesian Analysis, Kalman Filter

Random Forests for Complete Beginners victorzhou.com

Published April 15, 2019 under Data Science

Machine Learning, Random Forest

Comprehensive Python Cheatsheet github.io

Published April 9, 2019 under Python

Programming

How to Version Control Jupyter Notebooks nextjournal.com

Published April 9, 2019 under Python

Jupyter

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